The Effects of ESG Scores and ESG Momentum on Stock Returns and Volatility: Evidence from U.S. Markets
Bibliographic record
Abstract
The impact of Environmental, Social, and Governance (ESG) scores on financial performance remains a subject of debate, as the literature reports mixed evidence regarding their effect on stock returns. This research aims to examine the relationship between ESG ratings and the change in ESG scores, or ESG Momentum, concerning both returns and risk of a large sample of stocks traded on U.S. exchanges. The study examined a sample of 3856 stocks traded on U.S. exchanges, considering 20 years of quarterly data from December 2002 to December 2022. We applied multi-factor models and tested them through pooled ordinary, fixed effects, and random effects panel regression methods. Our results show negative relationships between ESG scores and stock returns and between ESG Momentum and volatility. Contrarily, we find positive associations between ESG Momentum and returns and between ESG scores and volatility. Although high ESG scores are generally associated with lower long-term stock returns, an increase in a company’s ESG rating tends to translate into immediate positive returns and reduced risk. Accordingly, investors may benefit from strategies that focus on companies actively improving their ESG performance, while firms themselves stand to gain by signaling continuous advancement in ESG-related areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".